Titlebook: Applied Pattern Recognition; Horst Bunke,Abraham Kandel,Mark Last Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Statistica.Wavel

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书目名称Applied Pattern Recognition影响因子(影响力)




书目名称Applied Pattern Recognition影响因子(影响力)学科排名




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书目名称Applied Pattern Recognition被引频次




书目名称Applied Pattern Recognition被引频次学科排名




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书目名称Applied Pattern Recognition读者反馈学科排名

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Facial Image Processing have attracted considerable attention in the last decades. In this chapter we focus on a strict view of facial image processing, i.e. transforming an input facial image into another and involving no high-level semantic classification like face recognition. A brief overview of facial image processin

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4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priorsatic segmentation of cardiac MR sequences where the blood pool of the left ventricle is segmented and important diagnostic measurements extracted from the segmentation. When used in a clinical setting, our algorithm could greatly alleviate the time that clinicians must spend working with the acquire

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Measuring Similarity Between Trajectories of Mobile Objectsis constantly growing. Mining spatio-temporal data can direct products and services to the right customers at the right time; it can also be used for resources optimization or for understanding mobile patterns. In this chapter, we cluster trajectories in order to find movement patterns of mobile obj

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Feature-Driven Emergence of Model Graphs for Object Recognition and Categorizations. In this sense, one may conceive the brain‘s data structure to have the form of graphs whose nodes are labeled with elementary features. These provide a versatile data format with the ability to render the structure of any mental object. Because of the multitude of possible object variations the g

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,Vorschriften für die Untersuchung,inations (shadows, high-lights, non-white lights) and in cluttered backgrounds. The LG Graph embeds both the local information (the shape of facial feature is stored within the local graph at each node) and the global information (the topology of the face). The LGG approach for detecting faces with

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